Week 8: Building the Dosage Table

Finally, we’ve reached the end product of my project, the result of all my hard work, the dosage table! To clarify, a dosage table is meant to tell you what amount of a specific drug you should take in order to achieve a specific subjective effect intensity.

The table was made through a two-step backwards calculation through the PK and PD models I’ve already built. The first step was to use the PD model to figure out the max plasma concentration for a specific target effect intensity. The sigmoid Emax can very easily be rearranged to solve for the concentration given the effect. I just plugged in my fitted parameters and solved. The second step converts the required concentration into the dosage through the PK model. However, rather than relying purely on the PK equation, I used a more direct approach by taking the observed peak concentration from each of my datasets. dividing that by the dose, then computing a weighted average. This gave me a scaling factor that represented how many ng/mL of psilocin each milligram of psilocybin produces at peak. This scale factor was a lot more reliable than a purely model-derived one, as it limits the exponential increase of errors, since it uses measured data as opposed to compounding on three estimated parameters.

The duration of how long the effect would last was calculated separately using the pharmacokinetic model. I put the threshold for “being high” at 6 ng/mL plasma concentration because that is what has been stated as the point at which users will feel the effects. Once the required dose is known, I simulate the full concentration-time curve for that dose and then measure how long the concentration stays above 6 ng/mL. This is a necessary step as it tells the clinician how long their patient will be experiencing the effects for and allows them to create an optimized therapy session. The resulting table is the following:

The table overall does align quite closely with values reported in the literature. One thing to notice, however, is that the predicted dose for the 90% target effect seems to be unusually high. This is because the model is extrapolating beyond available data, as the highest dose in my datasets is 30 mg, and the highest reported effects were in the 80s. As such, there is not much available data for the 90% range, and so the model has to estimate based on the equations I created. Additionally, the 20% row shows an estimated duration of 0 hours, but that’s mainly because its required Cmax itself is only 6.0 ng/mL, meaning there is likely very little time that the plasma concentration is actually above 6.0 ng/mL, and so the results say the estimated duration for a 20% effect is 0 hr.

The table is a population-level prediction, not a clinical recommendation. Individual pharmacokinetic variability means any individual’s response could differ meaningfully from these predictions. What the table offers is a starting point derived from 68 participants across 4 independent studies, which is more than has previously existed in a single synthesized form.

Next week, I will be doing validation! See you all then!